As more and more content is made available in the form of video, content aggregators are looking for ways to entice visitors to their sites. They want to be “the place” to go for information about categories that they choose to serve. This information is being generated in the form of videos that are either professionally developed or are user generated. As such, the video information is coming from a growing number of sources and creates problems for the content aggregator in terms of the number of sources of content and the volume of content that is being received.
The acquisition of the content is both labor intensive and time consuming. For example, methods exist to help in the automated classification of the content. However, a prerequisite for this and other video analysis (speech to text, object tracking, facial recognition, video piracy detection, etc.) is that the content must be made available to the various analysis tools. This means that the content must be acquired for input to the tools, from numerous sources, and in large volumes.
Known solutions to this problem include web sites that allow the user to enter the URL of a page that includes video. But, these sites have drawbacks in that they are manual processes and are very time consuming and labor intensive and do not, for example, provide many other features such as cataloging of the content. For example, in one known process, a user would search many different web sites and sources for relevant content. Once content is found, the user would record, e.g., write down, the URL and provide such information to another user for review. The other user then has to download the content and determine its relevancy. The other user would also have to determine whether the content even still exists. If the content exists and is relevant, the user then has to provide a description of the content. The URL and description can then be saved. However, as noted above, such processes are time consuming and, additionally, are prone to error. For example, the wrong URL can be matched to an incorrect or inaccurate description of the content. In this way, it may not be even possible to locate the content of interest, or may be difficult to discern which content is relevant.
Accordingly, there exists a need in the art to overcome the deficiencies and limitations described hereinabove.